The present invention relates generally to systems, methods, software, and graphical user interfaces for displaying and analyzing body images and for generating organ reports. More particularly, the present invention relates to graphical user interfaces and systems for analyzing one or more thoracic CT datasets to track and analyze lung nodules and other lung parameters.
Lung cancer is one of the most common forms of cancer among both men and women. Advances in medical imaging, such as CT and MRI scanning, have made it possible to localize and track early stage nodules that were previously non-detectable. However, such scanning protocols on a CT or MRI scanner typically generate no less than 40 images during a thoracic exam, while multi-slice protocols may generate 300 or more axial images. In order to analyze the dataset for lung nodules, the radiologist must review all of the slice images to localize the lung nodules. If a nodule is found in one slice image, the radiologist must then attempt to locate the nodule in the adjacent slices.
Unfortunately, such large amounts of data for each patient increases the probability that the radiologist will miss a potential nodule in their analysis of the image dataset, i.e., a “false negative.” Tumors may be too small to be reliably detected, or their appearance may be obscured by the surrounding tissues such as vessels. Missed tumors may be detected months or years later in a follow-up examination. During this interval the tumor may grow larger and, in the worst case, metastasize.
In their early stages of development, malignant lung tumors may not be detected even upon careful inspection of the image dataset. The early detection of lung cancer is of particular importance because the overall survival rate from the disease is very low. It is generally believed that early detection of cancer is beneficial, but in the case of lung cancer this is not established because of the recentness of the technique. The present invention provides tools which will help to elucidate this question.
To improve detection of lung nodules manual and semiautomatic pixel-based methods for segmenting CT images have been developed. One such method is manually creating a region of interest (ROI) delineating a nodule. A semiautomatic method requires a single, operator-defined seed point in which a computer algorithm will select similar contiguous gray-scale pixels that surround the seed point as the potential nodule. In another case it may be an operator-placed region of interest (such as a rectangle or ellipse) around the nodule.
While the proposed imaging methods offer significant potential to locate early stage nodules, still further improvements are desirable. In particular, if a small nodule or nodules are located in a first imaging scan, the radiologist will usually recommend that the patient return for a second, follow-up imaging scan. When the patient returns for the follow-up scan, the radiologist must relocate the nodules in the image scan and analyze the parameters of the nodules. Conventional imaging systems and methods do not provide an efficient way to determine if the nodules have increased in size, stayed the same, or the like.
Therefore, what is needed are reliable methods and devices which allow the radiologist to quickly and accurately localize and track any changes in nodules found in an imaging scan. Furthermore, what is needed is an improved method for visualization and characterization of small malignant lung tumors on thoracic image scan that would enable earlier detection of these tumors or nodules so as to enable earlier detection.